Garrett, since Anonymous reply was a little implicit, the point is that infants have a larger chance of dying before reproducing than young adults, so expected number of future offspring increases during childhood (when at each point counting only non-deceased children).
Aron, almost; it’s because they get older, and only future children are relevant. Whether they’ve had children won’t change the value except insofar it changes the chance for future children.
Me: …so IIUC, we expect a large influence of random variation in the sample.
Bzzzt! Wrong.
Upon more careful reading and thinking, what I understand the authors to be doing is this. They ask 436 Canadian subjects to imagine that two sons or two daughters of different specified ages died in a car accident, and ask which child the subject thinks the parent would feel more grief for. They then use the Thurstone scaling procedure to obtain a grief score for each age (1 day; 1, 2, 6, 10, 13, 17, 20, 30, 50 years).
They say that the procedure gives highly replicable results, and they have that large sample size, so no big sampling effects expected here.
They then correlate this data with reproduction value data for the same ages for the !Kung, which they got from: Howell, N. Demography of the Dobe !Kung, New York: Academic Press, 1979. This is not a random sample, it’s for the whole population, so no sampling effects there.
So replication with the same populations should give a very similar result. My original argument still applies, in that the high correlation may in part be due to the choice of populations, but I was completely wrong in expecting sampling effects to play a role.
Also, I realize now that I can’t really judge how extreme the correlation is (though I’ll happily defer to those who say it is very large): it’s too different from the usual kind of correlation in Psychology for my fledgling feeling for correlation values to apply. The usual kind of study looks at two values for each experimental subject (e.g. IQ vs. rating of looks) where this study looks at two values (Canadian ratings and !Kung reproductive value) for each of the ten age groups. In the usual kind of study, correlations >0.9 are suspiciously high, because, AFAIR, if you administer the same psychological instrument to the same subjects twice, a good correlation between the two tests is ~0.8, which means the noise from testing is just too large to get you a correlation >0.9. This obviously doesn’t apply to the present study’s design.
Garrett, since Anonymous reply was a little implicit, the point is that infants have a larger chance of dying before reproducing than young adults, so expected number of future offspring increases during childhood (when at each point counting only non-deceased children).
Aron, almost; it’s because they get older, and only future children are relevant. Whether they’ve had children won’t change the value except insofar it changes the chance for future children.
Bzzzt! Wrong.
Upon more careful reading and thinking, what I understand the authors to be doing is this. They ask 436 Canadian subjects to imagine that two sons or two daughters of different specified ages died in a car accident, and ask which child the subject thinks the parent would feel more grief for. They then use the Thurstone scaling procedure to obtain a grief score for each age (1 day; 1, 2, 6, 10, 13, 17, 20, 30, 50 years).
They say that the procedure gives highly replicable results, and they have that large sample size, so no big sampling effects expected here.
They then correlate this data with reproduction value data for the same ages for the !Kung, which they got from: Howell, N. Demography of the Dobe !Kung, New York: Academic Press, 1979. This is not a random sample, it’s for the whole population, so no sampling effects there.
So replication with the same populations should give a very similar result. My original argument still applies, in that the high correlation may in part be due to the choice of populations, but I was completely wrong in expecting sampling effects to play a role.
Also, I realize now that I can’t really judge how extreme the correlation is (though I’ll happily defer to those who say it is very large): it’s too different from the usual kind of correlation in Psychology for my fledgling feeling for correlation values to apply. The usual kind of study looks at two values for each experimental subject (e.g. IQ vs. rating of looks) where this study looks at two values (Canadian ratings and !Kung reproductive value) for each of the ten age groups. In the usual kind of study, correlations >0.9 are suspiciously high, because, AFAIR, if you administer the same psychological instrument to the same subjects twice, a good correlation between the two tests is ~0.8, which means the noise from testing is just too large to get you a correlation >0.9. This obviously doesn’t apply to the present study’s design.